These articles are between 3 and 5 year old, but are still valuable today. The methodology used in these articles is modern, and still state-of-the-art today. Some discuss immense data sets still available to the public, and that resulted in designing new machine learning techniques to handle them.

I am in the process of organizing these articles (written by myself) to eventually self-publish data science tutorials, in a few separate booklets, that are easy to understand for the layman with one year of data camp or college education in data science. The material will eventually be accessible to Data Science Central members, but not published in a traditional book.

My writing style has evolved over time: I have moved away from writing academic papers long ago, to most recently share advanced knowledge in a way that is accessible to beginners, sometimes even ground-breaking material, such asthis one. Most of what I write today is not taught in data camps or college textbooks. It provides an off-the-beaten-path introduction and expert advise in data science, in simple English, and even features advanced topics such as stochastic integral equations (the Wall Street's holy grail) or spatial random processes, yet accessible to professionals familiar with data sets but with little mathematical training. In short, this is a great next step after attending a standard statistics, machine learning, or data science curriculum.

Typically, the applications discussed are exciting, and the writing style is designed to make the reader willing to read more, as opposed to the dry writing style that plagues our profession. These articles cover topics such as quantum algorithms, high precision computing, Fintech, number theory, fake news / fake profile / fake reviews detection, cryptography, designing a better search engine, attribution modeling, cataloguing / taxonomy algorithms (NLP), clustering massive data sets, outliers handling, how to differentiate between correlation and causation, how to set up a business to sell data, and much more.